The built environment requires energy-flexible buildings to reduce energy peak loads and to maximize the use of (decentralized) renewable energy sources. The challenge is to arrive at smart control strategies that respond to the increasing variations in both the energy demand as well as the variable energy supply. This enables grid integration in existing energy networks with limited capacity and maximises use of decentralized sustainable generation. Buildings can play a key role in the optimization of the grid capacity by applying demand-side management control. To adjust the grid energy demand profile of a building without compromising the user requirements, the building should acquire some energy flexibility capacity. The main ambition of the Brains for Buildings Work Package 2 is to develop smart control strategies that use the operational flexibility of non-residential buildings to minimize energy costs, reduce emissions and avoid spikes in power network load, without compromising comfort levels. To realise this ambition the following key components will be developed within the B4B WP2: (A) Development of open-source HVAC and electric services models, (B) development of energy demand prediction models and (C) development of flexibility management control models. This report describes the developed first two key components, (A) and (B). This report presents different prediction models covering various building components. The models are from three different types: white box models, grey-box models, and black-box models. Each model developed is presented in a different chapter. The chapters start with the goal of the prediction model, followed by the description of the model and the results obtained when applied to a case study. The models developed are two approaches based on white box models (1) White box models based on Modelica libraries for energy prediction of a building and its components and (2) Hybrid predictive digital twin based on white box building models to predict the dynamic energy response of the building and its components. (3) Using CO₂ monitoring data to derive either ventilation flow rate or occupancy. (4) Prediction of the heating demand of a building. (5) Feedforward neural network model to predict the building energy usage and its uncertainty. (6) Prediction of PV solar production. The first model aims to predict the energy use and energy production pattern of different building configurations with open-source software, OpenModelica, and open-source libraries, IBPSA libraries. The white-box model simulation results are used to produce design and control advice for increasing the building energy flexibility. The use of the libraries for making a model has first been tested in a simple residential unit, and now is being tested in a non-residential unit, the Haagse Hogeschool building. The lessons learned show that it is possible to model a building by making use of a combination of libraries, however the development of the model is very time consuming. The test also highlighted the need for defining standard scenarios to test the energy flexibility and the need for a practical visualization if the simulation results are to be used to give advice about potential increase of the energy flexibility. The goal of the hybrid model, which is based on a white based model for the building and systems and a data driven model for user behaviour, is to predict the energy demand and energy supply of a building. The model's application focuses on the use case of the TNO building at Stieltjesweg in Delft during a summer period, with a specific emphasis on cooling demand. Preliminary analysis shows that the monitoring results of the building behaviour is in line with the simulation results. Currently, development is in progress to improve the model predictions by including the solar shading from surrounding buildings, models of automatic shading devices, and model calibration including the energy use of the chiller. The goal of the third model is to derive recent and current ventilation flow rate over time based on monitoring data on CO₂ concentration and occupancy, as well as deriving recent and current occupancy over time, based on monitoring data on CO₂ concentration and ventilation flow rate. The grey-box model used is based on the GEKKO python tool. The model was tested with the data of 6 Windesheim University of Applied Sciences office rooms. The model had low precision deriving the ventilation flow rate, especially at low CO2 concentration rates. The model had a good precision deriving occupancy from CO₂ concentration and ventilation flow rate. Further research is needed to determine if these findings apply in different situations, such as meeting spaces and classrooms. The goal of the fourth chapter is to compare the working of a simplified white box model and black-box model to predict the heating energy use of a building. The aim is to integrate these prediction models in the energy management system of SME buildings. The two models have been tested with data from a residential unit since at the time of the analysis the data of a SME building was not available. The prediction models developed have a low accuracy and in their current form cannot be integrated in an energy management system. In general, black-box model prediction obtained a higher accuracy than the white box model. The goal of the fifth model is to predict the energy use in a building using a black-box model and measure the uncertainty in the prediction. The black-box model is based on a feed-forward neural network. The model has been tested with the data of two buildings: educational and commercial buildings. The strength of the model is in the ensemble prediction and the realization that uncertainty is intrinsically present in the data as an absolute deviation. Using a rolling window technique, the model can predict energy use and uncertainty, incorporating possible building-use changes. The testing in two different cases demonstrates the applicability of the model for different types of buildings. The goal of the sixth and last model developed is to predict the energy production of PV panels in a building with the use of a black-box model. The choice for developing the model of the PV panels is based on the analysis of the main contributors of the peak energy demand and peak energy delivery in the case of the DWA office building. On a fault free test set, the model meets the requirements for a calibrated model according to the FEMP and ASHRAE criteria for the error metrics. According to the IPMVP criteria the model should be improved further. The results of the performance metrics agree in range with values as found in literature. For accurate peak prediction a year of training data is recommended in the given approach without lagged variables. This report presents the results and lessons learned from implementing white-box, grey-box and black-box models to predict energy use and energy production of buildings or of variables directly related to them. Each of the models has its advantages and disadvantages. Further research in this line is needed to develop the potential of this approach.
Twirre is a new architecture for mini-UAV platforms designed for autonomous flight in both GPS-enabled and GPS-deprived applications. The architecture consists of low-cost hardware and software components. High-level control software enables autonomous operation. Exchanging or upgrading hardware components is straightforward and the architecture is an excellent starting point for building low-cost autonomous mini-UAVs for a variety of applications. Experiments with an implementation of the architecture are in development, and preliminary results demonstrate accurate indoor navigation
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In the housing market enormous challenges exist for the retrofitting of existing housing in combination with the ambition to realize new environmentally friendly and affordable dwellings. Bio-based building materials offer the possibility to use renewable resources in building and construction. The efficient use of bio-based building materials is desirable due to several potential advantages related to environmental and economic aspects e.g. CO2 fixation and additional value. The potential biodegradability of biomaterials however demands also in-novative solutions to avoid e.g. the use of environmental harmful substances. It is essential to use balanced technological solutions, which consider aspects like service life or technical per-formance as well as environmental aspects. Circular economy and biodiversity also play an im-portant role in these concepts and potential production chains. Other questions arise considering the interaction with other large biomass users e.g. food production. What will be the impact if we use more bio-based building materials with regard to biodiversity and resource availability? Does this create opportunities or risks for the increasing use of bio-based building materials or does intelligent use of biomass in building materials offer the possibility to apply still unused (bio) resources and use them as a carbon sink? Potential routes of intelligent usage of biomass as well as potential risks and disadvantages are highlighted and discussed in relation to resource efficiency and decoupling concept(s).
The Cashing Cashew project focuses on isolation and purification of Cashew Nut Shell Liquid (CNSL) from Cashew Nut Shells (CNS) in order to fully utilize this valuable by-product of the cashew nut production. Global cashew nut production is about 4 million mt/ tons/yr. Of the cashew nut, about 70 % is shell that is removed in processing and currently typically burned as a dirty and inefficient fuel or discarded as waste. This is not only creating an environmental issue but also wasting valuable by-products. The shell contains circa 20-30 % brown viscous liquid, Cashew Nut Shell Liquid (CNSL). This natural resin contains valuable chemical components, for example, cardanol, cardol, and anacardic acid. CNSL and its derivatives have several industrial uses as for example biobased additives, polymeric building blocks, and biodiesel. Part of the CNSL can be extracted during the roasting process prior to separating the shell and nut kernel. The shell waste still has a high CNSL concentration that can be isolated by solvents or pressing (expeller). Expeller process is simple and not capital-intensive; therefore it is commonly used. The main disadvantages of the method are the high energy consumption and that 3-5 % oil remains in the press-cake producing harmful gases in burning. Also, the resulting cake is too dense to be further processed to charcoal or other useful application. The objective of this project is to study the purification of the CNSL obtained from pyrolytic isolation to find the most efficient way of making use of the CNSL oil and the total Cashew Nut Shell biomass. An initial evaluation of potential applications is also performed.
Façades have a high environmental and economic impact: they contribute 10-30% to GHG emissions and 30-40% of the building investment of new buildings [1]. Modern façades are highly optimized complex systems that consist of multiple components with varying life cycles [2]; however, many of the materials they employ are critical, and have a high CO2 footprint [3, 4]. New bio-composite facades products have emerged (a) whose mechanical properties are comparable to those of aluminum or glass fibre; (b) have a lower energy footprint; and (c) can fully or partially biodegrade [5]. Moreover, primary material sourcing from different waste streams can significantly lower the end products’ pricing. Still, their aesthetic qualities have not been sufficiently explored, so the scalability of their production remains limited. This project will develop specific combinations of bio-composites using food waste fillers and a biopolymer resin. Sheet samples will be made from these combinations and further tested against their mechanical properties, water resistance, aging and weathering. A Life Cycle Analysis will further consolidate the samples’ energy footprint. A new facade cladding tile product system with complex geometry using the overall best performing material composition will be designed and prototyped [17]. Emphasis will be given to the aesthetical properties of the tiles and their demountability. The system tiles will be further applied and tested at 1:1 scale, at The Green Village. During the project, an advisory board consisting of several companies within the building industry will be systematically consulted and their feedback will help the overall design process and their respective end products.
Introduction The research group Biobased Resources & Energy (BRE) of Avans focusses on recovery of valuable building blocks from low-value solid and liquid residual streams from agriculture, households and industries. For the valorisation of these residual streams, BRE looks into different biological, chemical and mechanical processes. One of the main issues in the utilisation of residual streams is economic feasibility and the recovery of multiple resources from one residual stream. Using membrane technologies in combination with biological, chemical and/or mechanical processes could offer great opportunities. Central Research Question What is the applicability of membrane technologies for valorisation of different residual streams and is it possible to integrate membrane technology in current and new biorefining projects of research group BRE: Set-up In order to reach the goal of this postdoc, 4 research questions will be answered using literature search, experimentation and modelling: 1) What membrane methods are currently (commercially) available to enhance the results of current projects in research group BRE? 2) What are the essential technical parameters for membrane separation and how can these be optimized? 3) What is the economic impact of using membrane technology in recovery of valuable building blocks from residual streams? 4) What are the effects of using membranes instead of or complementary to currently used methods on the sustainability of valorisation of residual streams? Cooperation The postdoc and the research group BRE want to extend the contact and research cooperation with (regional) businesses and (applied) universities and support and facilitate the introduction and further development of membrane technologies in the curriculum of different Avans study programmes. This will be done via internships, minor projects (together with businesses) and development of study material for courses and trainings.
